Library loading¶

In [1]:
import scanpy as sc, anndata as ad, numpy as np, pandas as pd
from scipy import sparse
from anndata import AnnData
import warnings
import socket
import holoviews as hv
import plotly.express as px
import seaborn as sns


from matplotlib import pylab
import sys
import yaml
import os
from pandas.api.types import CategoricalDtype
import plotly
import random
import itertools

import matplotlib.pyplot as plt

warnings.filterwarnings('ignore')
In [2]:
plotly.offline.init_notebook_mode()
In [3]:
%matplotlib inline
In [4]:
sc.settings.verbosity = 3         # verbosity: errors (0), warnings (1), info (2), hints (3)
sc.logging.print_header()
sc.settings.set_figure_params(dpi=50, facecolor='white')
pylab.rcParams['figure.figsize'] = (9, 9)

scanpy==1.8.1 anndata==0.7.6 umap==0.4.6 numpy==1.20.2 scipy==1.6.3 pandas==1.2.4 scikit-learn==0.24.2 statsmodels==0.13.1 python-igraph==0.9.8 louvain==0.7.1 pynndescent==0.5.5

Configure paths¶

In [5]:
hostRoot = "-".join(socket.gethostname().split('-')[0:2])

with open(os.path.expanduser('~')+"/paths_config.yaml", 'r') as f:
    paths = yaml.load(f, Loader=yaml.FullLoader)

#indir=paths["paths"]["indir"][hostRoot]
outdir="./outdir/"
FinaLeaf="/Cycling"
#projectBaseDir=paths["paths"]["projectBaseDir"][hostRoot]
adataPath = outdir+FinaLeaf+"/4C_Cycling_DA.h5ad"
adataRawPath = outdir+"/3_polaroid_quickAnno.h5ad"

figDir = "./figures/"
outBaseName = "04.2C_Cycling_inDepthCompositions"
badhuriMarkers =  ["FOS","DDIT3","CEBPD","HOPX","SUB1","HES4","NR2F1","NFIA","FOXO3","FOXP1","NFIB","TCF4","NFIX","HMGB2","SOX11","ZBTB18","BCL11A","ZNF462","ZIC2"]
RAmarkersPath = "./data/resources/RA_signalling.tsv"
RAmarkersDF = pd.read_csv(RAmarkersPath, sep="\t")
with open("./colorMap.yaml", 'r') as f:
    colorMap = yaml.load(f, Loader=yaml.FullLoader)["uns_colors"]

Load adata¶

In [6]:
adata = sc.read_h5ad(adataPath)
In [7]:
adata
Out[7]:
AnnData object with n_obs × n_vars = 2456 × 28371
    obs: 'dataset', 'organoid', 'region', 'type', 'type_region', 'regionContrast', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'total_counts_mt', 'log1p_total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'log1p_total_counts_ribo', 'pct_counts_ribo', 'n_genes', 'is.Stressed', 'leiden0.3', 'S_score', 'G2M_score', 'phase', 'Progenitor', 'subLeiden', 'umap_density_type', 'umap_density_region', 'umap_density_organoid'
    var: 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts', 'n_cells', 'highly_variable'
    uns: 'dataset_colors', 'diffmap_evals', 'draw_graph', 'leiden', 'neighbors', 'organoid_colors', 'pca', 'regionContrast_colors', 'region_colors', 'subLeiden_colors', 'type_colors', 'umap', 'umap_density_organoid_params', 'umap_density_region_params', 'umap_density_type_params'
    obsm: 'X_diffmap', 'X_draw_graph_fa', 'X_pca', 'X_umap'
    varm: 'PCs'
    obsp: 'connectivities', 'distances'

UMAP¶

In [11]:
sc.pl.umap(adata, color=["leiden0.3","type","region","subLeiden"] ,size = 100, add_outline = True,outline_width=(0.2, 0.05), frameon=False)

Boxplot of hexogenes¶

In [12]:
hexogenes = ["FGF8b.WPRE.SV40" ,  "SA.p2a.PURO.BGH", "p2a.dTom.rbGlob" ]
In [13]:
sns.set(rc={'figure.figsize':(6,3)})

sns.set_style("whitegrid")


for gene in  [m for m in hexogenes if m in adata.var_names]:
    adataGene = sc.read_h5ad(adataRawPath).raw.to_adata()[adata.obs_names,gene]
    dummies = pd.get_dummies(adataGene.obs["dataset"])
    
    countsAbove0 = (adataGene.X.todense().A1 > 0).astype(int)
    
    
    SegmentCounts = pd.DataFrame(np.dot(countsAbove0, dummies),columns=[gene+"_positive"])
    SegmentCounts["dataset"] = dummies.columns.tolist()
    SegmentCounts["organoid"] = SegmentCounts["dataset"].str.split("_", expand=True)[0]
    SegmentCounts["Segment"] = SegmentCounts["dataset"].str.split("_", expand=True)[1]

    SegmentCounts["Total_perOrganoid"] = adataGene.obs.groupby(["organoid"]).size().loc[SegmentCounts.organoid].tolist()
    SegmentCounts["Total_perSegment_perOrganoid"] = adataGene.obs.groupby(["dataset"]).size().loc[SegmentCounts.dataset].tolist()
    SegmentCounts[gene+"_positive.NormalizedByOrganoid"] = SegmentCounts[gene+"_positive"] / SegmentCounts["Total_perOrganoid"]
    SegmentCounts[gene+" positive cells rate"] = SegmentCounts[gene+"_positive"] / SegmentCounts["Total_perSegment_perOrganoid"]



    sns.boxplot(data=SegmentCounts, x="Segment", y=gene+" positive cells rate", 
                linewidth=.6,
                palette=dict(zip(SegmentCounts.Segment.unique().tolist(),[colorMap[k]["color"] for k in SegmentCounts.Segment.unique().tolist()])))
    plt.show()

By subleiden¶

In [14]:
sns.set(rc={'figure.figsize':(6,3)})

sns.set_style("whitegrid")

adatalocal = sc.read_h5ad(adataRawPath).raw.to_adata()[adata.obs_names]
for gene in  [m for m in hexogenes if m in adata.var_names]:
    adataGene = adatalocal[:,gene]

    adataGene.obs = adata.obs.copy()
    dummies = pd.get_dummies(adataGene.obs["subLeiden"])
    
    countsAbove0 = (adataGene.X.todense().A1 > 0).astype(int)
    
    
    print(adataGene.X.sum())
    
    
    SegmentCounts = pd.DataFrame(np.dot(countsAbove0, dummies),columns=[gene+"_positive"])
    SegmentCounts["subLeiden"] = dummies.columns.tolist()
    SegmentCounts["Total_subLeiden"] = adataGene.obs.groupby(["subLeiden"]).size().loc[SegmentCounts.subLeiden].tolist()
    #SegmentCounts["Total_perSegment_perOrganoid"] = adataGene.obs.groupby(["dataset"]).size().loc[SegmentCounts.dataset].tolist()
    #SegmentCounts[gene+"_positive.NormalizedByOrganoid"] = SegmentCounts[gene+"_positive"] / SegmentCounts["Total_perOrganoid"]
    SegmentCounts[gene+" positive cells rate"] = SegmentCounts[gene+"_positive"] / SegmentCounts["Total_subLeiden"]



    fig = px.bar(SegmentCounts, x="subLeiden", y=gene+" positive cells rate", color="subLeiden", 
                  height=800,width=1000, template="plotly_white",

                 color_discrete_map = dict(zip(adata.obs["subLeiden"].cat.categories,adata.uns["subLeiden_colors"])),title="## Normalized by region and subleiden",
    )
    
    fig.update_traces(marker_line_color='black',
                  marker_line_width=1, opacity=1)
    fig.update_layout( yaxis = dict( tickfont = dict(size=30), title_font=dict(size=30)),legend = dict(font = dict(size = 30)), 
                  xaxis = dict( tickfont = dict(size=30), title_font=dict(size=30)))
    
    fig.show()

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